Boosting Disaster Preparedness

In a new innovative collaboration, the World Bank has selected the University of Salford’s THINKlab to produce a roadmap for implementing an impact-based early warning system in the country. This decision marks a significant step forward in advancing the country’s disaster management, preparedness, and response capabilities.

By 2030, with current climate projections, the world will face some 560 disasters per year. An additional estimated 37.6 million people will be living in conditions of extreme poverty due to the impacts of climate change and disasters by 2030.

Now the University of Salford’s THINKlab is working in the Kalutara District of Sri Lanka to pilot its MOBILISE digital platform and help to build climate resilience and community-based multi-hazard early warning systems. The project will help local agencies and communities to work together to build their local resilience against climate-induced disasters such as landslides and floods.

Working with key disaster management agencies in Sri Lanka, their goal is to build a comprehensive framework that offers advanced impact-based forecasting and early warning systems that can offer anticipatory actions to the community to protect lives and their livelihoods.

Director of the THINKlab, Professor Terrence Fernando, who leads the work in the country said: “This project is all about building local community resilience in each district by working with the national agencies, district secretaries, divisional secretaries, officers on the ground and the community to fully prepare for climate-induced hazards in order to reduce their impact on the communities, infrastructure and the local economy. We are developing technologies to help decision-makers understand the local risks, monitor and prepare for emerging hazards and communicate early warnings in advance to the community so that the impact of hazards on them can be minimised.  Our technology solutions are leading the way for evidence-based decision-making and impact-based multi-hazard forecasting and early warnings”.

Central to this collaboration will be the deployment of THINKlab's state-of-the-art MOBILISE Solutions, which will help pave the way for more effective and adaptive approaches to Sri Lanka’s disaster management practices. The THINKlab will also develop a comprehensive training plan to empower officers engaged in disaster risk reduction activities as well as communities to use mobile solutions to become active participants in impact-based early warnings.

Professor Fernando added: “We hear notions such as “whole society approach to disaster risk reduction” and “No one should be left behind” during disasters. However, the implementation of such noble concepts requires strengthening local partnerships, understanding local risks, gathering information on vulnerable people and implementing well-thought-out standard operational procedures before and during disasters.  Our projects and the technology are all about developing and implementing socio-technical systems that can offer such support for the local partners”.

As a part of the project, funded by the Climate Adaptation and Resilience (CARE) program and the UKRI in the UK, THINKlab has been instrumental in developing an Urban Laboratory called “Kalutara Living Lab” to build partnerships and local digital capacity in responding to hazards such as floods and landslides over the last two years, in collaboration with Sri Lanka’s National Building Research Organisation and Tecxal Systems. This innovative concept has already shown promising results in building climate resilience. Due to its success, the World Bank has now decided to scale up the solutions and approaches developed by THINKlab and NBRO during the coming year with the view to scaling it up across the whole of Sri Lanka in the near future.  The plan of this work is to show this work as an exemplar of how the technology can be used as a catalyst for supporting evidence-based disaster risk reduction as well as implementing impact-based multi-haard forecasting and early warning systems for anticipatory actions.